Music Recommendations Based on Real-Time Data

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dc.contributor.authorAurén, Marcus
dc.contributor.authorBååw, Albin
dc.contributor.authorHagerman Olzon, David
dc.contributor.authorKarlsson, Tobias
dc.contributor.authorNilsson, Linnea
dc.contributor.authorShirmohammad, Pedram
dc.contributor.departmentChalmers tekniska högskola / Institutionen för data- och informationsteknik (Chalmers)sv
dc.contributor.departmentChalmers University of Technology / Department of Computer Science and Engineering (Chalmers)en
dc.date.accessioned2019-07-03T14:55:09Z
dc.date.available2019-07-03T14:55:09Z
dc.date.issued2018
dc.description.abstractThis thesis describes the development, implementation and results of a music recommender system that utilizes real time data, namely time and heart rate, for the recommendations. The recommender system was made by combining two systems, the recommender system which predicts a number of song features for a specific user and a ranking system which finds the best matching tracks for these features. Three implementations of the recommender system were implemented for comparison, namely Deep Neural Network, Contextual Bandit and Linear Regression. These implementations were tested with offline evaluation which showed that for our problem, a contextual bandit model had the best accuracy.
dc.identifier.urihttps://hdl.handle.net/20.500.12380/256144
dc.language.isoeng
dc.setspec.uppsokTechnology
dc.subjectData- och informationsvetenskap
dc.subjectComputer and Information Science
dc.titleMusic Recommendations Based on Real-Time Data
dc.type.degreeExamensarbete för kandidatexamensv
dc.type.degreeBachelor Thesisen
dc.type.uppsokM2
local.programmeDatateknik 300 hp (civilingenjör)
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